no code implementations • 8 Sep 2022 • Innyoung Kim, Sejin Kim, Donghyun You
Despite automation in mesh generation using either an empirical approach or an optimization algorithm, repeated tuning of meshing parameters is still required for a new geometry.
no code implementations • 4 Nov 2021 • Seungpyo Hong, Sejin Kim, Donghyun You
For the deep-RL to successfully learn the control policy, accurate and ample data of the dynamics are required.
no code implementations • 10 Oct 2021 • Sejin Kim, Innyoung Kim, Donghyun You
A multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed for the first time using deep reinforcement learning.
no code implementations • 5 Dec 2018 • Mario Rüttgers, Sangseung Lee, Donghyun You
Tracks of typhoons are predicted using a generative adversarial network (GAN) with observational data in form of satellite images and meteorological data from a reanalysis database.
Atmospheric and Oceanic Physics
no code implementations • 16 Aug 2018 • Mario Rüttgers, Sangseung Lee, Donghyun You
Tracks of typhoons are predicted using satellite images as input for a Generative Adversarial Network (GAN).
no code implementations • 21 Dec 2017 • Sangseung Lee, Donghyun You
Unsteady flow fields in the future at a Reynolds number which is not in the training datasets are predicted using a GAN.
Fluid Dynamics